Executive insight from the analysis
At a national level, the waiting list remains under sustained pressure, with approximately
730,000 patients waiting as of October 2025. While the proportion of long-waiters (≥12 months)
has fallen meaningfully since early 2023, the absolute number of patients waiting over a year
remains high at more than 126,000.
This creates a mixed picture. The system has become better at preventing waits from worsening,
but it has not yet created enough sustained capacity to reduce the overall backlog. The data
suggests structural pressure rather than short-term volatility.
OP and IPDC pathway insight
Analysis of OP and IPDC pathways shows distinct dynamics. Outpatient (OP) waiting lists
account for the majority of total volume and drive most of the month-to-month volatility,
particularly in high-demand medical specialties. In contrast, IPDC waiting lists are
smaller in volume but contribute disproportionately to long-wait severity.
This indicates that OP growth reflects demand pressure, while IPDC backlogs reflect
constrained downstream capacity. Treating both pathways with the same intervention
risks addressing symptoms rather than causes.
What the data suggests should change
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Shift success measures away from total waiting numbers and towards ≥12-month cohorts.
The long-wait ratio and severity index trends show that focusing on headline volumes alone
can mask persistent structural delay.
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Concentrate effort on a small number of high-impact specialties.
The Top-10 specialty analysis shows that Dermatology and Orthopaedics account for a
disproportionate share of long waits, meaning marginal capacity added elsewhere will
have limited system-wide effect.
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Differentiate OP and IPDC interventions.
OP backlogs benefit most from demand smoothing and access expansion,
while IPDC backlogs require sustained theatre, bed, and staffing capacity to reduce
long waits meaningfully.
Practical actions informed by the dashboard
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Ring-fence additional capacity for patients waiting 12–18 months and 18+ months,
rather than distributing activity evenly across all waiting bands.
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Use hospital-level concentration analysis to target intervention.
The Top-10 hospital and Pareto charts show that a small number of hospitals
(e.g. [hospital name]) account for the majority of long-waiters over the selected period.
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Use county-level variation to guide regional action.
Counties such as Galway and Cork consistently exhibit higher long-wait ratios,
suggesting that regional rebalancing may deliver more impact than national blanket measures.
Data source and preparation
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Data was sourced from publicly available HSE waiting list publications covering Outpatient (OP)
and Inpatient/Day Case (IPDC) services.
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Separate annual datasets for OP and IPDC were collected and consolidated into a single
longitudinal dataset to enable time-based analysis.
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Data was merged at both specialty level and hospital level to support national, regional,
and service-specific insights.
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Additional contextual fields, including county and province, were integrated to enable
geographic deep-dives and regional comparison of waiting list pressure.
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Prior to analysis, the data was cleaned and standardised. This included harmonising date
formats, aligning specialty and hospital naming conventions, validating totals against
waiting-time bands, and removing incomplete records.
Data governance, quality and provenance
The analysis is based on HSE administrative waiting list snapshot data from January 2023
to October 2025. A total of 9,418 records were analysed after preprocessing, with no
missing values detected in critical analytical fields.
All transformations and metrics are reproducible. For operational deployment,
data lineage, version control, and ownership should be formally documented
to ensure transparency and auditability.
Limitations and risks
Forecasts are trend-based and assume continuity of recent patterns.
They do not capture sudden policy changes, workforce shocks, or one-off initiatives.
The severity index highlights system pressure and should not be interpreted as clinical risk.
Next steps
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Set explicit reduction targets for ≥12-month waits in the highest-strain specialties
and track progress monthly using this dashboard.
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Extend forecasting to specialty- and hospital-level scenarios to support targeted,
evidence-based capacity planning rather than national averages alone.
